Why AI Gets Things Wrong Print

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The mechanism behind the errors.

WHAT THESE SYSTEMS DO

Generate text that is statistically likely given what came before.

Not retrieve facts. Not reason to conclusions. Predict plausible text.

WHY THAT PRODUCES ERRORS

Plausible and true are different properties.

Where the model lacks information, plausible text is still produced.

WHY IT SOUNDS CERTAIN

Fluency and accuracy are separate. The model optimises for the first.

Training data is mostly confident prose, so the output is confident prose.

WHAT THIS MEANS

You cannot judge reliability from how the answer sounds.

WHERE ERRORS CONCENTRATE

Specific figures Names, dates and places Citations and references Recent events Obscure subjects Arithmetic

WHERE THEY ARE RARER

Explanations of well-established concepts Answers about material you provided

Language tasks: rewriting, summarising, translating

THE PRACTICAL RESPONSE

Verify in proportion to consequence. Supply the facts yourself where they matter.


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